Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization

• URL: http://arxiv.org/abs/2101.04073v2
• Date: Wed, 13 Jan 2021 14:57:12 GMT
• Title: Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
• Authors: Anush Sankaran, Olivier Mastropietro, Ehsan Saboori, Yasser Idris, Davis Sawyer, MohammadHossein AskariHemmat, Ghouthi Boukli Hacene
• Abstract summary: We introduce a black-box framework, Deeplite Neutrino for production-ready optimization of deep learning models. The framework is easy to include in an existing production pipeline and is available as a Python Package. The framework is currently used in production and the results and testimonials from several clients are summarized.
• Score: 2.762905634186996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive accuracy, it creates a lot of logistical challenges and unreasonable resource requirements during development and deployment. This has been one of the key reasons for deep learning models not being excessively used in various production environments, especially in edge devices. There is an immediate requirement for optimizing and compressing these deep learning models, to enable on-device intelligence. In this research, we introduce a black-box framework, Deeplite Neutrino for production-ready optimization of deep learning models. The framework provides an easy mechanism for the end-users to provide constraints such as a tolerable drop in accuracy or target size of the optimized models, to guide the whole optimization process. The framework is easy to include in an existing production pipeline and is available as a Python Package, supporting PyTorch and Tensorflow libraries. The optimization performance of the framework is shown across multiple benchmark datasets and popular deep learning models. Further, the framework is currently used in production and the results and testimonials from several clients are summarized.

Related papers

• Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion [4.164728134421114]
  We introduce AutoFusion, a framework that fuses distinct model parameters for multi-task learning without pre-trained checkpoints. We validate AutoFusion's effectiveness through experiments on commonly used benchmark datasets. Our framework offers a scalable and flexible solution for model integration, positioning it as a powerful tool for future research and practical applications.
  arXiv  Detail & Related papers  (2024-10-08T07:21:24Z)
• MALPOLON: A Framework for Deep Species Distribution Modeling [3.1457219084519004]
  MALPOLON aims to facilitate training and inferences of deep species distribution models (deep-SDM) It is written in Python and built upon the PyTorch library. The framework is open-sourced on GitHub and PyPi.
  arXiv  Detail & Related papers  (2024-09-26T17:45:10Z)
• Leveraging Reinforcement Learning and Large Language Models for Code Optimization [14.602997316032706]
  This paper introduces a new framework to decrease the complexity of code optimization. The proposed framework builds on large language models (LLMs) and reinforcement learning (RL) We run several experiments on the PIE dataset using a CodeT5 language model and RRHF, a new reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2023-12-09T19:50:23Z)
• CoLLiE: Collaborative Training of Large Language Models in an Efficient Way [59.09824823710863]
  CoLLiE is an efficient library that facilitates collaborative training of large language models. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization.
  arXiv  Detail & Related papers  (2023-12-01T08:02:16Z)
• VeLO: Training Versatile Learned Optimizers by Scaling Up [67.90237498659397]
  We leverage the same scaling approach behind the success of deep learning to learn versatiles. We train an ingest for deep learning which is itself a small neural network that ingests and outputs parameter updates. We open source our learned, meta-training code, the associated train test data, and an extensive benchmark suite with baselines at velo-code.io.
  arXiv  Detail & Related papers  (2022-11-17T18:39:07Z)
• Data Summarization via Bilevel Optimization [48.89977988203108]
  A simple yet powerful approach is to operate on small subsets of data. In this work, we propose a generic coreset framework that formulates the coreset selection as a cardinality-constrained bilevel optimization problem.
  arXiv  Detail & Related papers  (2021-09-26T09:08:38Z)
• Top-KAST: Top-K Always Sparse Training [50.05611544535801]
  We propose Top-KAST, a method that preserves constant sparsity throughout training. We show that it performs comparably to or better than previous works when training models on the established ImageNet benchmark. In addition to our ImageNet results, we also demonstrate our approach in the domain of language modeling.
  arXiv  Detail & Related papers  (2021-06-07T11:13:05Z)
• Conditional Generative Modeling via Learning the Latent Space [54.620761775441046]
  We propose a novel framework for conditional generation in multimodal spaces. It uses latent variables to model generalizable learning patterns. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes.
  arXiv  Detail & Related papers  (2020-10-07T03:11:34Z)
• Coresets via Bilevel Optimization for Continual Learning and Streaming [86.67190358712064]
  We propose a novel coreset construction via cardinality-constrained bilevel optimization. We show how our framework can efficiently generate coresets for deep neural networks, and demonstrate its empirical benefits in continual learning and in streaming settings.
  arXiv  Detail & Related papers  (2020-06-06T14:20:25Z)
• Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning [3.479254848034425]
  We present Deep-n-Cheap -- an open-source AutoML framework to search for deep learning models. Our framework is targeted for deployment on both benchmark and custom datasets. Deep-n-Cheap includes a user-customizable complexity penalty which trades off performance with training time or number of parameters.
  arXiv  Detail & Related papers  (2020-03-27T13:00:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.